---
title: 'CrewAI'
description: 'AgentOps and CrewAI teamed up to make monitoring Crew agents dead simple.'
---

## Video Tutorial

<iframe
  width="560"
  height="315"
  src="https://www.youtube.com/embed/t9YRRd2-wus"
  title="AgentOps + CrewAI Integration Tutorial"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
  allowfullscreen
></iframe>

[CrewAI](https://www.crewai.com/) is a framework for easily building multi-agent applications. AgentOps integrates with CrewAI to provide observability into your agent workflows. Crew has comprehensive [documentation](https://docs.crewai.com) available as well as a great [quickstart](https://docs.crewai.com/how-to/Creating-a-Crew-and-kick-it-off/) guide.

## Installation
Install AgentOps and CrewAI, along with `python-dotenv` for managing API keys:

<CodeGroup>
  ```bash pip
  pip install agentops crewai python-dotenv
  ```
  ```bash poetry
  poetry add agentops crewai python-dotenv
  ```
  ```bash uv
  uv pip install agentops crewai python-dotenv
  ```
</CodeGroup>

## Setting Up API Keys

You'll need API keys for AgentOps and OpenAI (since CrewAI's built-in `LLM` uses OpenAI models by default):
- **OPENAI_API_KEY**: From the [OpenAI Platform](https://platform.openai.com/api-keys)
- **AGENTOPS_API_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)

Set these as environment variables or in a `.env` file.

<CodeGroup>
  ```bash Export to CLI
  export OPENAI_API_KEY="your_openai_api_key_here"
  export AGENTOPS_API_KEY="your_agentops_api_key_here"
  ```
  ```txt Set in .env file
  OPENAI_API_KEY="your_openai_api_key_here"
  AGENTOPS_API_KEY="your_agentops_api_key_here"
  ```
</CodeGroup>

Then load them in your Python code:
```python
from dotenv import load_dotenv
import os

load_dotenv()

AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
```

## Usage

Simply initialize AgentOps at the beginning of your CrewAI application. AgentOps automatically instruments CrewAI components—including its `LLM`—to track your agent interactions.

Here's how to set up a basic CrewAI application with AgentOps:

```python
import agentops
from crewai import Agent, Task, Crew, LLM

# Initialize AgentOps client
agentops.init()

# Define the LLM to use with CrewAI
llm = LLM(
    model="openai/gpt-4o",  # Or your preferred model
    temperature=0.7,
)

# Create an agent
researcher = Agent(
    role='Researcher',
    goal='Research and provide accurate information about cities and their history',
    backstory='You are an expert researcher with vast knowledge of world geography and history.',
    llm=llm,
    verbose=True
)

# Create a task
research_task = Task(
    description='What is the capital of France? Provide a detailed answer about its history, culture, and significance.',
    expected_output='A comprehensive response about Paris, including its status as the capital of France, historical significance, cultural importance, and key landmarks.',
    agent=researcher
)

# Create a crew with the researcher
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    verbose=True
)

# Execute the task
result = crew.kickoff()

print("\nCrew Research Results:")
print(result)
```

## Examples

<CardGroup cols={2}>
  <Card title="Job Posting" icon="notebook" href="/v2/examples/crewai" newTab={true}>
    Create job postings with a crew of specialized agents
  </Card>
  <Card title="Markdown Validator" icon="notebook" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/crewai/markdown_validator.ipynb" newTab={true}>
    Validate and improve markdown content using CrewAI agents
  </Card>
</CardGroup>


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